Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 507230
Title A Minimax Regret Analysis of Flood Risk Management Strategies Under Climate Change Uncertainty and Emerging Information
Author(s) Pol, T.D. van der; Gabbert, S.; Weikard, H.P.; Ierland, E.C. van; Hendrix, E.M.T.
Source Environmental and Resource Economics 68 (2017)4. - ISSN 0924-6460 - p. 1087 - 1109.
DOI http://dx.doi.org/10.1007/s10640-016-0062-y
Department(s) Environmental Economics and Natural Resources Group
WIMEK
WASS
Operations Research and Logistics
Publication type Refereed Article in a scientific journal
Publication year 2017
Keyword(s) Adaptive management - Climate change - Flexibility - Flood risk - Learning - Minimax regret - Robust optimisation
Abstract

This paper studies the dynamic application of the minimax regret (MR) decision criterion to identify robust flood risk management strategies under climate change uncertainty and emerging information. An MR method is developed that uses multiple learning scenarios, for example about sea level rise or river peak flow development, to analyse effects of changes in information on optimal investment in flood protection. To illustrate the method, optimal dike height and floodplain development are studied in a conceptual model, and conventional and adaptive MR solutions are compared. A dynamic application of the MR decision criterion allows investments to be changed after new information on climate change impacts, which has an effect on today’s optimal investments. The results suggest that adaptive MR solutions are more robust than the solutions obtained from a conventional MR analysis of investments in flood protection. Moreover, adaptive MR analysis with multiple learning scenarios is more general and contains conventional MR analysis as a special case.

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